Super Duper Complex Python Algorithm

import random
import threading
import time
from concurrent.futures import ThreadPoolExecutor

# Helper functions for distance and 2-opt swap.
def euclidean_distance(p1, p2):
    return math.hypot(p1[0] - p2[0], p1[1] - p2[1])

def total_distance(tour, cities):
    return sum(euclidean_distance(cities[tour[i]], cities[tour[(i + 1) % len(tour)]])
               for i in range(len(tour)))

def two_opt_swap(tour, i, k):
    new_tour = tour[:i] + tour[i:k + 1][::-1] + tour[k + 1:]
    return new_tour

# The TSPProblem class encapsulates the problem data.
class TSPProblem:
    def __init__(self, cities):
        self.cities = cities
        self.num_cities = len(cities)
    
    def random_tour(self):
        tour = list(range(self.num_cities))
        random.shuffle(tour)
        return tour

# The Solver class uses simulated annealing with embedded 2-opt improvement.
class TSPSolver:
    def __init__(self, problem, init_temp=1000, cooling_rate=0.995, min_temp=1e-3, max_iter=10000):
        self.problem = problem
        self.init_temp = init_temp
        self.cooling_rate = cooling_rate
        self.min_temp = min_temp
        self.max_iter = max_iter
        self.best_tour = None
        self.best_distance = float('inf')
        self.lock = threading.Lock()  # For thread-safe update of best tour
    
    def accept_probability(self, delta, temperature):
        # Accept worse solutions with a probability that decreases with temperature.
        return math.exp(-delta / temperature) if delta > 0 else 1
    
    def evaluate_neighbor(self, current_tour, temperature):
        # Use a local 2-opt move to generate a neighbor.
        n = len(current_tour)
        i, k = sorted(random.sample(range(n), 2))
        new_tour = two_opt_swap(current_tour, i, k)
        new_distance = total_distance(new_tour, self.problem.cities)
        current_distance = total_distance(current_tour, self.problem.cities)
        delta = new_distance - current_distance
        return new_tour, new_distance, delta
    
    def anneal(self):
        current_tour = self.problem.random_tour()
        current_distance = total_distance(current_tour, self.problem.cities)
        best_local_tour = current_tour
        best_local_distance = current_distance
        
        temperature = self.init_temp
        iteration = 0
        
        # Use a thread pool for evaluating neighbor moves in parallel.
        with ThreadPoolExecutor(max_workers=4) as executor:
            while temperature > self.min_temp and iteration < self.max_iter:
                # Submit several neighbor evaluations in parallel.
                futures = [executor.submit(self.evaluate_neighbor, current_tour, temperature)
                           for _ in range(4)]
                
                # Wait for all moves and pick the best improvement among them.
                for future in futures:
                    neighbor_tour, neighbor_distance, delta = future.result()
                    # Acceptance decision based on simulated annealing criterion.
                    if delta < 0 or random.random() < self.accept_probability(delta, temperature):
                        current_tour, current_distance = neighbor_tour, neighbor_distance
                        # Check if this is the best found so far.
                        if current_distance < best_local_distance:
                            best_local_tour = current_tour
                            best_local_distance = current_distance
                # Cooling schedule.
                temperature *= self.cooling_rate
                iteration += 1

                # Thread-safe global best update.
                with self.lock:
                    if best_local_distance < self.best_distance:
                        self.best_distance = best_local_distance
                        self.best_tour = best_local_tour[:]
                
                if iteration % 1000 == 0:
                    print(f"Iteration {iteration}: Best distance so far = {self.best_distance:.2f}")
        return self.best_tour, self.best_distance

# A complex main function that sets up the problem and runs the solver.
def main():
    # Generate random cities within a 1000x1000 grid.
    num_cities = 50
    cities = [(random.uniform(0, 1000), random.uniform(0, 1000)) for _ in range(num_cities)]
    
    # Create the TSP problem instance.
    problem = TSPProblem(cities)
    
    # Initialize the solver with a high initial temperature and slow cooling.
    solver = TSPSolver(problem, init_temp=1000, cooling_rate=0.995, min_temp=1e-3, max_iter=10000)
    
    start_time = time.time()
    best_tour, best_distance = solver.anneal()
    end_time = time.time()
    
    print("\nFinal best tour:")
    print(best_tour)
    print(f"Final best distance: {best_distance:.2f}")
    print(f"Time taken: {end_time - start_time:.2f} seconds")
    
if __name__ == '__main__':
    main()